Devices and system for channeling and automatic monitoring of fluid flow in fluid distribution systems

ABSTRACT

A fluid manifold is capable of channeling and monitoring fluid flow within a fluid distribution system. The manifold includes one or more input lumens. Each input lumen associated with a respective inlet port. The manifold also includes a plurality of output lumens such that each output lumen is associated with a respective outlet port and at least one input lumen is coupled to two or more output lumens. The manifold also includes one or more flow sensors capable of measuring fluid flow parameters of fluid flowing through at least one of the one or more input lumens and the plurality of output lumens. In some implementations, a flow sensor can be mounted to each output lumen of the manifold.

RELATED APPLICATION

This application is a continuation under 35 U.S.C. § 111(a) ofInternational Application No. PCT/US2015/045320, filed Aug. 14, 2015,which claims the benefit under 35 U.S.C. § 119 of U.S. ProvisionalApplication No. 62/037,511, entitled “Devices and System for AutomaticMonitoring of Water flow in Plumbing Systems” and filed on Aug. 14,2014. The entire contents of the foregoing applications are herebyincorporated herein by reference.

BACKGROUND

A rapidly rising global population is putting increasing pressure onnatural resources. Demand for food, water, and energy is expected torise by 30-50% over the next 20 years. Limited availability of waterresources has implications for both the new development of real estateas well for the continued use of already-developed property.

New real estate development opportunities may be increasingly limited bywater availability. Existing or anticipated water shortages may causeregulators to restrict or prohibit housing development. For example, theState of California currently requires water agencies to withholdapproval for developments until a determination is made that sufficientwater resources exist to serve a proposed development for a period of 20years.

SUMMARY

According to at least one aspect, a fluid manifold is capable ofchanneling and monitoring fluid flow within a fluid distribution systemthat may be internal or external to a building, home or apartment. Themanifold includes one or more input lumens. Each input lumen isassociated with a respective inlet port. The manifold also includes aplurality of output lumens such that each output lumen is associatedwith a respective outlet port and at least one input lumen is coupled totwo or more output lumens. The manifold also includes one or more fluidflow sensors capable of measuring fluid flow parameters of fluid flowingthrough at least one of the one or more input lumens and the pluralityof output lumens. The fluid flow sensor(s) can be an ultrasonic flowsensor, hall effect flow sensor, electromagnetic flow sensor, ormechanical flow sensor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are conceptual diagrams of example multi-port fluidmanifolds.

FIG. 2 shows a diagram illustrating ultrasonic signal propagationbetween ultrasonic transducers of a fluid meter.

FIGS. 3A and 3B show diagrams illustrating a first example manifold 300and a respective approach for mounting transducer blocks thereon.

FIG. 4 shows a diagram of a manifold with an over-under crossoverconfiguration.

FIGS. 5A and 5B show diagrams illustrating various views of a manifoldwith another crossover configuration.

FIGS. 5C and 5D show diagrams of the manifold 500 in FIGS. 5A and 5Bwithout the manifold cover.

FIG. 6 shows a diagram illustrating assembly of the manifold shown inFIGS. 5A-5D.

FIGS. 7A and 7B show diagrams illustrating a first crossover segment ofa manifold similar to that shown in FIGS. 5A-5D and 6.

FIGS. 8A and 8B show diagrams illustrating a second crossover segment ofa manifold similar to that shown in FIGS. 5A-5D and 6.

FIGS. 9A and 9B show diagrams illustrating other configurations ofmanifold output lumens.

FIG. 10A shows a plot depicting an example water flow rate over timeassociated with time overlapping water flow events.

FIG. 10B shows multiple plots of probability distributions (e.g.,probability density functions) of total water usage perappliance/fixture event.

FIGS. 11A-11C show plots illustrating water flow rates recorded forvarious water flow events driven by fixtures and/or appliancesassociated with FIG. 10B.

FIG. 12 shows multiple plots of water flow rate probabilitydistributions (e.g., probability density functions) associated withvarious fixtures and appliances.

FIG. 13 shows a flow diagram illustrating a factorial hidden Markovmodel (FHMM).

FIG. 14 shows a flow chart illustrating a process for estimating astatistical model or device signatures.

FIG. 15 shows a flow diagram illustrating a process for estimating flowevents based on observation data.

DETAILED DESCRIPTION

A fluid distribution system can include fluid manifolds (such as watermanifolds) capable of channeling fluid flowing within one lumen (such asa pipe) to a plurality of other lumens. According to the currentdisclosure, one or more flow sensors can be integrated in (or mountedto) a fluid manifold. The flow sensor(s) can allow for monitoring fluidflow rate, monitoring cumulative fluid usage, detecting fluid leaks or acombination thereof.

FIG. 1A is a conceptual diagram of an example multi-port fluid manifold100 (such as a water manifold). The fluid manifold 100 includes an inletport 110 and a plurality of outlet ports 120. While FIG. 1A shows asingle inlet port 110, the manifold 100 generally can include one ormore inlet ports 110 with each inlet port coupled to one or morerespective outlet ports. Fluid can enter the manifold 100 via the inletport 110 and flows out of the manifold 100 via the outlet ports 120. Ina water distribution system, for instance, the inlet port 110 can becoupled to a water main line, service line or water heater (such as aheating ventilating and air conditioning (HVAC) boiler). In a gasdistribution system, for instance, the inlet port 110 can be coupled toa gas main line or service line. The outlet ports 120 can be coupled torespective lumens (such as pipes) serving a plurality of destinations.For a water distribution system, such destinations can include internalor external irrigation systems, hose connections or plumbing systemswithin one or more apartments, rooms, devices (e.g., fixtures,appliances, or other devices), a kitchen, bathroom, laundry room or acombination thereof. For a natural gas distribution system, thedestinations can include a stove, heater, chimney or a combinationthereof. Pipes that can be coupled to the inlet port 110 and/or theoutlet port 120 can include plastic pipes (such as cross linkedpolyethylene (PEX) pipes, polyvinyl chloride (PVC) pipes, ChlorinatedPVC (CPVC) pipes, or other plastic pipes) or metal pipes (such copperpipes, stainless steel pipes, galvanized steel pipes or other metalpipes). Pipes can be coupled to the inlet port 110 and/or outlet ports120 via respective pipe fittings (such as copper fittings, chromefittings, black fittings, PVC fittings, bronze fittings, galvanizedfittings, SHARKBITE®, Gaterbite, ProBite, other push-fit plumbingfittings or other fittings) and/or pipe connectors such as copperconnectors, carbonsteel connectors, PEX connectors, polybutylene (PB)connectors, SHARKBITE® connectors, push connectors, or other connectors)corresponding outlet ports 120, each of which can be connected to piping(e.g., PEX piping) via an optional quick connect fitting.

In some implementations, at least one of the inlet and outlet ports canbe associated with an integrated flow sensor capable of measuring theflow rate of the fluid flowing into the inlet port(s) 110 or out of theoutlet ports 120. As illustrated in FIG. 1A, flow sensors that can beintegrated in (or mounted to) the manifold 100 can include ultrasonicsensors 130 (described below with respect to FIGS. 2 and 3), hall-effectsensors, Lorentz force (or magnetic) sensors 140, impeller sensors 150,other non-invasive or inline flow sensors, or a combination thereof. ALorentz force sensor 140 can include a magnetic field source 142disposed in-line with the fluid flow, such as within a branch couplingan inlet port 110 to one of the respective outlet ports 120. Chargedparticles in the fluid (such as water or natural gas) flowing past themagnetic field source 142 create an electric field that varies with theflow rate. Electrodes 144 and 146 arranged on opposite sides of theoutlet port 120 can sense the variations in the electric field andtherefore provide measurements that are indicative of the flow rate.Example impeller sensors 150 can include an impeller blade 152 disposedin-line with the fluid flow, such as within a branch coupling an inletport 110 to one of the respective outlet ports 120. Fluid flowing pastthe impeller blade 152 causes the impeller blade 152 to spin at a rateproportional to the fluid flow rate. Alternative impeller sensors 150may include positive displacement, mutating disk, multi-jet and turbineimpellers as known in the art. An ultrasonic sensor (or ultrasonic flowmeter) 130 can include two or more ultrasonic transducers (such asthin-film or disk piezoelectric transducers or CMUT MEMS sensors).Variations in propagation times of ultrasonic signals traveling throughthe fluid between distinct transducers can depend (e.g., can be linearlydependent) on the fluid flow rate. Measuring differences in signalpropagation times (for instance between downstream and upstream signalsor between zero-flow and upstream/downstream signals) allows forestimating corresponding fluid flow rate(s). Ultrasonic flow meters aredescribed in further details with regard to FIG. 2.

FIG. 1B shows an example manifold prototype 100 b with two inlet ports110, each of which is coupled to a respective plurality of (such asfive) outlet ports 120. The manifold 100 b can be employed as a watermanifold such that one of the inlet ports 110 (and outlet ports 120coupled thereto) can be employed to channel cold water while the otherinlet ports 110 (and outlet ports 120 coupled thereto) can be employedto channel hot water. The manifolds 100 a and 100 b are referred toherein as manifold(s) 100. Manifolds described in this disclosure suchas manifold 100) can be made of polyethylene (PE), polyvinyl chloride(PVC), copper, other material or a combination of various materials. Ingeneral, a manifold includes a lumen (or chamber with at least one inlet110 port and two or more outlet ports 120.

In water distribution systems, manifolds control the distribution of andcold water by channeling water to different rooms and/or fixtures in ahouse, apartment building, shopping mall, or other structure. Largerstructures, such as apartment buildings, may include multiple manifolds,e.g., one manifold per apartment. In some cases, a single manifold canbe employed to distribute both hot and cold water through separate hotand cold manifold chambers (lumens). In other cases, a waterdistribution system can include separate hot and cold water manifolds(e.g., manifolds with a single inlet port 110 each). In either case,cold water enters the corresponding cold-water manifold, or manifoldchamber, from the water main or service line, and hot water enters thecorresponding hot-water manifold, or manifold chamber, from the waterheater, which is also supplied by the water main or service line. Theservice line, water main, or water heater maintains water pressure inthe manifold.

Typically, manifolds are installed near the water heater duringconstruction for ease of access. Each manifold (or manifold chamber)includes multiple output ports (outlets) that connect to piping, such ascross-linked polyethylene (REX) piping or other type of piping. PEXpiping can be connected to the manifold output ports via pipe connectorsor pipe fittings as known in the art of plumbing. The piping can channelwater from the manifold to rooms such as kitchen, bathroom and laundryroom or to individual fixtures (such as sinks, dishwashers, showers,bath tubs, toilets) and appliances (such as washing machines and dishwashers). In some implementations, at least one of the inlet ports 110can include (or be coupled to) a gate valve (such as a shutoff valve)for stopping water flow into (and out of) the manifold. In someimplementations, each outlet port 120 can include a respective gatevalve, therefore, allowing to stop water from flowing out of each outletport separately. For instance, if a particular fixture served by arespective outlet port 120 is under repair or the pipe coupled to thefixture breaks, the gate valve coupled to that outlet port can shut offthe water flow towards that fixture. In some implementations, a subsetof inlet and/or outlet ports of a valve can include (or be coupled to)respective gate valves. Gate valves can be controlled manually orautomatically, e.g., in response to flow rate data collected by flowrate sensors integrated into (or mounted to) the inlet and/or outletports of the manifold. The number and size of the manifold output ports,type of fitting(s), type of gate valve(s), and type and size of pipingcan depend on the installation and/or the particular structure to wherethe manifold is to be installed.

Once the water manifold and piping are installed, the flow sensorsassociated with the water manifold can be activated to monitor waterflow to the fixtures throughout the structure. In some implementations,each outlet port can be associated with (e.g., include or be coupled to)a respective flow sensor such as an ultrasonic flow sensor or animpeller-based flow sensor. The manifold can also include additionalflow sensor(s) associated with the inlet port(s) for measuring waterentering the manifold from the service line or water heater.

FIG. 2 shows a diagram illustrating ultrasonic signal propagationbetween ultrasonic transducers of a fluid meter. The fluid meterincludes a pair of transducers 210 a and 210 b (also referred tohereinafter as transducer(s) 210) and a controller 250 coupled thereto.The transducers 210 can be mounted in a non-invasive or invasive mannerto the wall of a lumen 201 (such as a tailpiece or a pipe). Thecontroller 250 can be a microprocessor, microcontroller, microchip,application-specific integrated circuit (ASIC), digital signal processor(DSP), specialized analog circuit or a combination thereof. Thecontroller 250 can be part of or coupled to the fluid meter. Thecontroller 250 can be configured to access signals transmitted orreceived by the transducers 210 and compute fluid flow parameter valuessuch as flow or flow rate) based on the accessed signals.

Each transducer 210 can be oriented at an angle θ with respect to thelumens longitudinal axis. The configuration (e.g., the orientation andthe relative placement with respect to the wall of the lumen) of thetransducers 210 can define the propagation direction of ultrasonicsignals transmitted or received by the transducers 210. According to a“V” configuration (as shown in FIG. 2), an ultrasonic signal transmittedby the transducer 210 a can propagate at the same direction (e.g., at anangle θ with respect to the lumen longitudinal axis) as the orientationof the transducer 210 a, reflect back from the inside of the inner wallof the lumen 201 and be received by the transducer 210 b at a directiondefined by the orientation of the transducer 210 b. According to a “W”configuration, an ultrasonic signal transmitted by a first transducer210 can exhibit three reflections off the inner wall of the lumen 201before reaching the second transducer 210. In some implementations, thetransducers 210 can be arranged across each other such that theultrasonic signals propagate directly (without wall reflections) fromone transducer 210 to the other. For instance, according to a “Z”configuration, the transducers 210 can be arranged across each other onthe outer wall of the lumen 201. In some implementations, each of thetransducers 210 can be configured to operate alternately as atransmitter and receiver. In some implementations, one of thetransducers 210 can be configured to operate as a dedicated transmitter,while the other transducer 210 can be configured to operate as adedicated receiver.

A transmit (TX) transducer (e.g., transducer 210 a) can transmit ahigh-frequency burst, or pulse, into the lumen 201 and a receive (RX)transducer (e.g., transducer 210 b) can receive a version of the pulseafter some time delay (with respect to transmit time). The time delaybetween the transmit time (e.g., time at which transmission of the pulsestarts) and the receive time (time at which reception by the RXtransducer starts) represents the propagation time of the transmittedsignal between the TX and RX transducers 210. The signal propagationtime depends on the fluid flow velocity. For instance, the propagationtime of a signal propagating upstream (against fluid flow) is longerthan the propagation time of a signal propagating in no-flow fluid.Also, the propagation time of a signal propagating downstream (alongfluid flow) is shorter than the propagation time of a signal propagatingin no-flow fluid.

In some implementations, the increase in signal propagation time (forinstance with respect to signal propagation time in a no-flow state) dueto fluid flow velocity can be linearly dependent on (or linearlyproportional to) the velocity of the fluid. For water flowing withinpipes, such linearity is satisfied at least for a range of flow ratessuch as from about 0 gallons per minute (GPM) to about 1 GPM, ¾ GPM, ½GPM, ¼ GPM or other flow rate values greater than (or less than) 1 GPM.As such, fluid flow velocity can be computed based on measured signalpropagation times. Also, given that flow rate is linearly dependent onthe flow velocity and the cross sectional area of the lumen 201, thefluid flow rate can be computed based on respective measured signalpropagation time(s). In some implementations, the controller 250 candetermine the fluid flow rate (or fluid flow velocity) based on measuredsignal propagation time using a lookup table (LUT). Using a LUT canallow for determination of the fluid flow rate based on measured signalpropagation time even if the relationship between the increase in signalpropagation time (due to fluid flow) and the flow rate is nonlinear.

In some implementations, the controller 250 can determine the signalpropagation time based on cross-correlation values between the RX signal(or samples thereof) and delayed versions of a waveform representing theTX signal. For instance, the controller 250 can determine the signalpropagation time to be equal to the time delay of the delayed version ofthe transmitted waveform associated with the maximum cross-correlationvalue. In some implementations, the controller 250 can determine thesignal propagation time based on cross-correlation values between the RXsignal (or samples thereof) and a plurality of templates such that eachtemplate corresponds to a respective value of the signal propagationtime. The templates can be representations of delayed and/or filteredversions of the waveform. For instance, in generating the templates, thewaveform can be filtered in a way to reflect potential distortions(caused by the fluid, the transducers, the tailpiece or a combinationthereof) associated with the RX signal. In some implementations, aplurality of filters may employed such that a separate filter is used togenerate a respective subset of templates. In some implementations, thecontroller 250 can determine the signal propagation time by measuringthe time difference between transmit time and the time at which the RXsignal crosses a predetermined threshold value at the receivingtransducer (e.g., transducer 210 b). The controller 250 can alsodetermine the signal propagation time by measuring the difference inphase, or phase delay, between the transmitted and received signals.

The controller 250 can compute the fluid flow velocity or fluid flowrate using the difference in signal propagation times associated with asignal transmitted upstream and a signal transmitted downstream. In suchcase, each of the transducers 210 can be configured to operatealternately as transmitter and receiver. The controller 250 can alsocompute the fluid flow velocity or fluid flow rate using the differencein signal propagation times associated with a signal transmittedupstream (or downstream) and a signal transmitted in a no-flow fluidstate. In some implementations, both time differences are linearlydependent on the fluid flow velocity and fluid flow rate. The controller250 can determine the difference in signal propagation times associatedwith upstream and downstream signals (or upstream and zero-flow signalsor downstream and zero-flow signals) by computing cross-correlationvalues between respective upstream and downstream RX signals. In someimplementations, the controller 250 can determine the difference insignal propagation times associated with upstream and downstream signals(or upstream and zero-flow signals or downstream and zero-flow signals)by computing difference in phase between such signals in the frequencydomain.

In some implementations, the ultrasonic fluid meter can include (or becoupled to) an analog-to-digital converter (ADC) configured to samplereceived ultrasonic signals. A processor (such as a DSP) can thenprocess the sampled signals to determine fluid flow velocity or fluidflow rate. In determining the fluid flow velocity or fluid flow rate,the processor can be configured to perform filtering, cross-correlation,zero-crossing, phase-based methods based on FFT or Goertzel algorithm,template matching or a combination thereof. In some implementations, theultrasonic fluid flow meter can include a custom application specificintegrated circuit (ASIC) configured to perform the zero-crossing orphase detection techniques for differential time-of-arrival estimationdirectly.

In some implementations, the fluid flow meter can include more than twotransducers. For instance, the fluid flow meter can include multipletransducer pairs such that each transducer pair is mounted inassociation with a respective inlet or outlet port of a manifold (suchas manifold 100). The transducer pairs can be coupled to the controller250. The controller 250 can determine fluid flow parameters (such asflow rate or flow velocity) for various inlet and/or outlet ports basedon RX signals received by transducers associated with each inlet oroutlet port. For instance, using RX signals for a pair of transducersmounted in association with an outlet port of a manifold, the controller250 can determine the flow velocity or flow rate through that outletport. The transducers can be mounted to (or integrated into) a manifoldaccording to invasive or non-invasive settings. The controller 250 canbe embedded within a casing (or housing) of the manifold or can bearranged outside the manifold casing.

The practicality and functionalities of a manifold (such as a watermanifold) can vary significantly based on respective design parametersand features, such as the number of inlet and outlet ports of themanifold, the size of the manifold, the sensors and/or other devicesintegrated in (or coupled to) the manifold and/or the material of themanifold. For instance, water manifolds are typically installed betweenwall studs. As such, a water manifold that is “too wide” can lead toundesired digging (or cutting) through wall studs when plumbing themanifold. Also, as the number of inlet and/or outlet ports per manifoldincreases, a smaller number of manifolds can be employed per fluiddistribution system, therefore, leading to simpler plumbing. Also,manifolds can become “smart” with integrated sensors, processors,communication interfaces or other electronic/electromechanical devices.“Smart” manifolds can allow for automatic fluid consumption monitoring,leak detection, or remote control of fluid flow directed to separateapparatuses such as fixtures and/or appliances associated with a fluiddistribution system.

FIGS. 3A and 3B show diagrams illustrating a first example manifold 300and a respective approach for mounting transducer blocks thereon. Themanifold 300 includes two inlet chambers 305 a and 305 b (also referredto herein individually or in combination as inlet chamber(s) 305 orinlet lumen(s) 305) associated with two respective inlet ports 310 a and310 b (also referred to herein individually or in combination as inletport(s) 310). The inlet chambers 305 a and 305 b can be arranged onealongside the other (e.g., substantially in parallel to each other). Theinlet chamber 305 a is coupled to a respective plurality of outlet ports320 a through respective output branches 325 a. The inlet chamber 305 bis coupled to a respective plurality of outlet ports 320 b throughrespective output branches 325 b. The output branches 325 a and 325 bare also referred to herein, individually or in combination, as outputbranch(es) 325. The outlet ports 320 a and 320 b are also referred toherein, individually or in combination, as outlet port(s) 320.

In the manifold 300, the output branches 325 a (coupled to inlet chamber305 a) are arranged to extend in a direction away from both inletchambers 305 a and 305 b such that the respective outlet ports 320 a arearranged on the same side as the inlet chamber 305 a to which they arecoupled. Also, the output branches 325 b (coupled to inlet chamber 305b) are arranged to extend away from both inlet chambers 305 a and 305 bsuch that the respective outlet ports 320 b are arranged on the sameside as the inlet chamber 305 b to which they are coupled. In someimplementations, each output branch can include (or be associated with)a respective manual or automatic shutoff valve (not shown in FIGS. 3Aand 3B). The shutoff valves can be arranged within the transversecylindrical structures 327. Each output branch 325 can include two snapelements 326 that form a “Z” shape together with that output branch 325.As shown in FIG. 3B, a transducer block 360 can be mounted onto anoutput branch 325 between the respective two snap elements 326. That is,a transducer block 360 can be positioned onto the output branch 325between the respective snap elements 326. As such, the transducers 360associated with the transducer block 350 are arranged across each otheron opposite sides of the output branch 325 in accordance with a “Z”configuration. The snap elements 326 are capable of snapping (orarranged to snap) onto the transducer block 350.

Arranging the transducers 360 across each other on opposite sides of theoutput branch 325 allows for respective ultrasonic signals to traveldirectly from one transducer 360 to another across the fluid in theoutput branch 325 without reflecting from the inner wall of the outputbranch 325. In some implementations, the transducer block 350 can bemounted to the output branch 325 such that respective ultrasonic signalpath is at about 45 degrees with respect the longitudinal axis of theoutput branch 325. For instance, the angle between the signal path andthe longitudinal axis of the output branch 325 can be between 30 to 60degrees. In some implementations, the angle between the signal path andthe longitudinal axis of the output branch 325 can be greater than zeroand smaller than 90 degrees. In some implementations, the transducers360 can be mounted to the output branches 325 according to a “V”configuration or any other configuration known in the art.

FIG. 4 shows a diagram of a manifold 400 with an over-under crossoverconfiguration. The manifold 400 includes two input chambers (or inputlumens) 405 a and 405 b (also referred to as input chamber(s) 405) thatare arranged one alongside the other. Each of the inlet chambers 405 aand 405 b is associated with a respective inlet port 410 a or 410 b. Theinput chamber 405 a is coupled to a respective plurality of outlet ports420 a through respective output branches 425 a. The output branches 425a extend from the respective input chamber 405 a across (e.g., over orunder) the other input chamber 405 b such that the respective outletports 420 a are arranged on the side of the input chamber 405 b. Theinput chamber 405 b is coupled to a respective plurality of outlet ports420 b through respective output branches 425 b. The output branches 425b extend from the respective input chamber 405 b across (e.g., over orunder) the other inlet chamber 405 a such that the respective outletports 420 b are arranged on the side of the input chamber 405 a. Thecrossover configuration (i.e., output branches for one inlet chamberextending across the other inlet chamber) allows for a narrower manifold(i.e., smaller width) compared to a non-crossover configuration as shownin FIG. 3A. For instance, while the manifold 300 is 8.25 inches wide,the manifold 400 is only 6.625 inches wide. Reducing the width of amanifold, for instance, without reducing the number of respective inletor outlet ports, can reduce the space occupied by that manifold wheninstalled and therefore simplify the respective plumbing process. Inother words, reducing the width of the manifold decreases the chances ofany undesired drilling through wall studs or any other structuresallowing the manifold to be placed in areas that are space constrainedand distributed throughout the plumbing system (for example, under asink, behind a washer, below the floor where space is constrained.

In some implementations, each output branch 425 can include (or beassociated with) a respective manual or automatic shutoff valve (notshown in FIG. 4). The shutoff valves can be arranged within thetransverse cylindrical structures 427. In some implementations,ultrasonic transducers such as transducers 360) can be mounted to one ormore of the output branches 425 according to a “Z” configuration, a “V”configuration, or other configuration known in the art. In someimplementations, ultrasonic transducers can be mounted to at least oneof the inlet chambers 405 a and 405 b.

FIGS. 5A and 5B show diagrams illustrating various views of a manifold500 with another crossover configuration. In FIG. 5A, the drawing (a)represents atop view of the manifold 500 while drawings (b) through (d)represent distinct side views of the same manifold 500. FIG. 5B shows abottom view of the manifold 500. The manifold 500 includes two inletchambers (or inlet lumens) 505 a and 505 b arranged one alongside theother. Fluid (such as water) can flow into inlet chamber 505 a throughthe respective inlet port 510 a and into inlet chamber 505 b through therespective inlet port 510 b. The inlet chamber 505 a is coupled to arespective plurality of outlet ports 520 a arranged on an opposite sideof the manifold 500 from the inlet chamber 505 a. The inlet chamber 505b is coupled to a respective plurality of outlet ports 520 b arranged onan opposite side of the manifold 500 from the inlet chamber 505 b. Thatis, output branches coupling inlet chamber 505 a to respective outletports 520 a are arranged to crossover (or extend past) inlet chamber 505b towards respective outlet ports 520 a. Also, output branches couplinginlet chamber 505 b to respective outlet ports 520 b are arranged tocrossover (or extend past) inlet chamber 505 a towards respective outletports 520 b. The output branches coupling the inlet chambers 505 a and505 b to respective outlet ports 520 a and 520 b, respectively, arehidden in FIGS. 5A and 5B by the manifold cover (or manifold housing)530.

FIGS. 5C and 5D show diagrams of the manifold 500 in FIGS. 5A and 5Bwithout the manifold cover. The manifold 500 can include a circuit board535 (e.g., including processor(s), ADC(s) and/or other circuitcomponents). The manifold 500 can include one or more sensors 540, suchas pressure sensor(s), water quality sensor(s), fluid temperaturesensor(s) or other types of sensors. For instance, a pressure sensor anda water quality sensor can be integrated in each of the inlet lumens 505a and 505 b. In some implementations, pressure sensors (and/or othersensors) can be integrated in association with the outlet ports 520 aand 520 b (such as integrated within respective output branches 525). Insome implementations, the manifold 500 can include shutoff valves 536 aand 536 b (such as electromechanical shutoff valves) for controllingfluid flow through the inlet lumens 505 a and 505 b. For instance, theshutoff valve 536 a can control fluid flow through inlet lumen 505 a,while the shutoff valve 536 b can control fluid flow through inlet lumen505 b. In some implementations, the manifold 500 can include shutoffvalves 529 (such as ¼ turn shutoff valves) integrated in associationwith the output branches 525. For instance, each output branch 525 caninclude a transverse cylindrical structure 527 for housing a shutoffvalve 529. In some implementations, the shutoff valves 529 can beintegrated with the output branch through other mechanisms know in theart. In some implementations, the shutoff valves 529 can be automaticshutoff valves controlled by one or more control circuits orcontrollers).

In some implementations, the manifold 500 can be manufactured as aplurality of components that can be assembled together to form themanifold 500. Each of the inlet lumens 505 a and 505 b can include aplurality of lumen segments, or modules, such as adapter segments 570and crossover segments 571) assembled into a respective lumen (orchamber). Each crossover segment 571 includes a respective output branch525 coupled thereto and providing a respective outlet port such asoutlet port 520 a or 520 b). For each crossover segment 571, arespective transducer assembly (or transducer block) 550 can be mountedto the output branch 525 of that crossover segment 571. The transducerassembly 550 can include two or more transducers that are coupled (e.g.,through electric wires) to the circuit board 535. Adapter segments 570can be arranged at both ends of a lumen (such as inlet lumen 505 a and505 b). In some implementations, adapter segments 570 can be arrangedbetween crossover segments 571. In some implementations, one or moresensors 540 can be integrated within (or coupled to) a segment 570 (forinstance, the segment 570 that is nearest to the respective inlet port510 a or 510 b). Plugs (such as national pipe thread (NPT) plugs) 577can be employed to shut the end ports of the lumen segments 570 arrangedat the end of the inlet lumens 505 a and 505 b, therefore, providingterminations of such lumens. These lumens may be extended with modulesto control water flow for external systems such as lawn irrigationsystems allowing for a complete “smart plumbing” system that can measureflow, accumulation and leaks in irrigations systems external to thebuilding, single-family home or apartment. The manifold 500 (or aprocessor thereof) can be coupled to a communication network throughwired or wireless communication capabilities (such as antenna,communication interface, etc.). The manifold 500 can access theInternet, through the communication network, to obtain external datasuch as weather data to control external irrigation system and wateramounts.

FIG. 6 shows a diagram illustrating assembly of the manifold 500 shownin FIGS. 5A-5D. For each lumen (such as inlet lumen 505 a or 505 b), therespective adapter segments 570 and the respective crossover segments571 can be held together through one or more rods 575. In someimplementations, each lumen segment (such as adapter segment 570 andcrossover segment 571) can include one or more holes runninglongitudinally across the lumen segment. For instance, each lumensegment can include four longitudinal holes at four respective corners.When a set of lumen segments are aligned together longitudinally, therespective longitudinal holes are also aligned with each other. The rods575 can go through the aligned holes from one end of the manifold 500all the way to the other end (e.g., to side of the inlet ports 505 a and505 b). At the other end of the manifold 500 (e.g., to side of the inletports 505 a and 505 b), locking nuts 578 can be employed at the ends ofthe rods 575 to compress and hold the respective manifold assemblytogether. For instance, the rods 575 can be threaded at one end in orderto be able to engage the locking nuts 578.

As shown in FIG. 6, the inlet ports 510 a and 510 b of the manifold 500can be coupled to each other, such as both inlet ports can be part of asingle component 580. In some implementations, the component 580 caninclude the inlet ports 510 a and 510 b) and respective shutoff valves536 a and 536 b. When assembling the manifold 500, a separate set oflumen segments (such as adapter segments 570 and crossover segments 571)can be aligned with and coupled to a respective inlet port (of the inletports 510 a and 510 b), therefore, resulting into two inlet lumens 505 aand 505 b that are arranged alongside each other and are coupled to theinlet ports 510 a and 510 b, respectively.

FIGS. 7A and 7B show diagrams illustrating a first crossover segment 700for use in a manifold 500 similar to that shown in FIGS. 5A-5D and 6.The crossover segment 700 includes a hollow component 710 and an outputbranch 725 providing a respective outlet port 720. The hollow component710 represents a portion of the inlet lumen of the respective assembledmanifold 500. The hollow component 710 provides a threaded cylindricalportion 715 for engaging another crossover segment 700 or an adaptersegment such as adapter segment 570 shown in FIGS. 5D and 6). The outputbranch 725 is coupled to the hollow component 710 and provides arespective outlet port 720. The output branch 725 provides a fluid flowpath to the respective outlet port 720 from the hollow component 710. Atthe end of the output branch 725, the outlet port 720 includes athreaded cylindrical portion 726 capable of engaging a pipe, pipefitting or pipe connector.

The output branch 725 can include two snap elements 723 that arearranged to form a “Z” shape together with the output branch 725. Thesnap elements 723 are arranged to hold a respective transducer assembly750. The transducer assembly 750 can include a transducer block (ortransducer saddle) 755 that is arranged to snap fit into the “Z”configuration formed by the two snap elements 723 and the output branch725. The transducer block 755 is capable of holding a pair of ultrasonictransducers 760. When the transducer block 755 is mounted on the outputbranch, the transducers 760 are arranged across each other on differentsides of the output branch 725. As such, ultrasonic signal can propagatedirectly from one transducer 760 to another across the output branch 725without necessarily reflecting from the inner wall of the output branch725. The transducer block 755 can be made of a polymer or othermaterial.

The output branch 725 can also include a shutoff valve 772 arrangedtransverse to the longitudinal axis of the output branch 725. Forinstance, the output branch 725 can include a cylindrical structure 771(or a hole within the output branch wall) for housing the shutoff valve772. In some implementation, the shutoff valve can be a manual valve(such as a ¼ turn valve). In some implementations, the shutoff valve 772can be an electromechanical valve controlled by a control circuit.

FIGS. 8A and 8B show diagrams illustrating a second crossover segment800 for use in a manifold 500 similar to that shown in FIGS. 5A-5D and6. The crossover segment 800 includes a hallow component 810representing a portion of an inlet lumen in the respective assembledmanifold, an outlet port 820 and an output branch 825 coupling thehallow component 810 to the outlet port 820. In some implementations,the outlet port 820 can be coupled to the output branch 825 through acylindrical structure 871 arranged for housing a shutoff valve 872. Theshutoff valve 872 can be a manual valve (such as a ¼ turn valve or anelectromechanical shutoff valve that is controllable by a controlcircuit or a controller.

A transducer assembly including a transducer block (or transducersaddle) 855 and a pair of transducers 860 can be mounted to the outputbranch 825. The transducer block 855 can include a panel (or slab) andtwo end-components 857 arranged transversely at both ends of the panel856. The panel 856 can be arranged substantially parallel to (or to siton) a longitudinal side of the output branch 825 while theend-components 857 grab onto transverse sides of the output branch 825.The transducers 860 can be positioned at the outer surfaces of theend-components 857. As such, when the transducer block 855 is mounted onthe output branch 825, the transducers 860 are arranged on oppositesides of the output branch 825, therefore, allowing for ultrasonicsignals to propagate directly between the two transducers withoutnecessarily reflecting from the inner wall of the output branch 825. Thetransducer block 855 can be made of a polymer, copper, or othermaterial.

FIGS. 9A and 9B show diagrams illustrating other configurations ofmanifold output branches. FIG. 9A shows an output branch (or outputlumen) 925 a forming a “V” shape. The transducers 960 can be arrangedacross one of the segments (of the output branch 925 a) forming the “V”shape. FIG. 9B shows another output branch (or output lumen) 925 bforming a “U” shape. The transducers 960 can be arranged across one ofthe segments (of the output branch 925 b) forming the “U” shape (such asthe horizontal segment). In some implementations, the output branchconfigurations can be employed in any of the manifolds described in thisdisclosure (such as manifolds 100 a, 100 b, 300, 400 or 500.

While the manifolds 300, 400 or 500 are described as includingultrasonic flow sensors, any other type of flow sensors can be employedinstead of (or in combination with) the ultrasonic sensors. In someimplementations, different types of flow sensors can be employed (ormounted) at distinct output lumens. Each of the manifolds described inthis disclosure can include (or be coupled to) one or more processorsconfigured to process or monitor data collected by the flow sensors(and/or other sensors), control any electronic or electromechanicaldevices associated with the manifold (such as automatic shutoff valves),communicate with external devices (such as computer servers, onlineservers, mobile devices, tablets, laptops, desktops, etc.), or acombination thereof. For instance, the processor can be configured toestimate fluid flow rate (or fluid flow velocity) based on signalsmeasured by the transducers. The manifold can also include at least onememory to store computer code instructions executable by theprocessor(s), data collected by the flow sensors or other sensors, orother data. The manifold can also include a wireless (or othercommunication) interface (e.g., a Zigbee, Bluetooth, or Wi-fi interface)for communicating with an external device. For instance, thecommunication interface can be configured to transmit data collected bythe flow sensors or other sensors e.g., water temperature sensors,pressure sensors, water quality sensors) to an external electronicdevice (such as a server, Internet server, cloud server, mobile device,tablet, laptop, personal computer, etc.) through a network connection(e.g., Ethernet, dial-up), or a radio link (WiFi, cellular, Zigbee).

The processor(s) can be configured to interface with a server via thecommunication interface (such as wireless interface), and the server mayprovide a dashboard (via a smart phone or other networked device) thatindicates fluid usage such as water usage) statistics based on flow ratedata collected by the flow sensors. Flow rate data and water usagestatistics can be used to reduce water consumption through analysis ofusage patterns, elimination of waste, leak detection, and incentives forlowered water use. The processor(s) can obtain data, such flow rate orflow velocity data, based on signals received from the flow sensors andeither stores the data in local memory, transmits the data to a remotememory or server, or both. In some embodiments, the processor(s) cantransmit the data to a server via a wireless interface (e.g., a Zigbee,Bluetooth, or Wi-fi interface). The sensors, processor(s), memory,and/or wireless interface can be powered by a battery, a power line, or,for manifolds installed outdoors, a solar cell. The flow sensors mayalso have a passive wake-up feature for power reduction; that is, theymay only draw power when water flows through the manifold or arespective outlet port. The manifold may also include a turbine or otherdevice that harvests energy from flowing water to obviate need forbattery or wall power.

Upon receiving the data from the processor, the server can store thedata in a water usage database. Engines (possibly embodied ascomputer-readable instructions on a nonvolatile storage medium) cancompute water usage statistics and present these water usage statisticsto homeowners, renters, building owners, property managers, utilitiesmanagers, and other users via management dashboards. In someimplementations, the water usage statistics can be computed by theprocessor(s) embedded in (or coupled to) the manifold. The dashboardscan be displayed via web browsers or special-purpose applications oncomputer monitors, smart phones (e.g., iPhones, Blackberries, andDroids), laptop computers (including iPads), or any other suitabledisplay. In some implementations, the manifold sensors can be combinedwith other meters and sensors to form a distributed sensor network thatcan be used to meter individual units in an apartment complex,individual businesses in a shopping mall, or individual homes andbusinesses in a utility service area. Data collected from such adistributed sensor network provides information on aggregate waterusage, individual water usage, and statistics and patterns related towater usage in a given building or water usage zone.

In some implementations, the flow sensors can be configured to calibratethemselves using readings from the main water meter, branch water meters(including meters within the same building), inline leak detectors,and/or data from the water usage database. Self calibration can allowfor accurate flow measurement and leak detection.

In some implementations, the dashboards can provide real-timecomparisons/rankings within and among social networks, neighborhoods,cities, counties, states, and nationwide using water usage statisticsderived from flow rate sensor data. Data can also be used (for instanceby a computer application) to calculate and compare water footprints, tovisualize water usage data, to compute variance in water usage for agiven fixture or building, and to reward consumers for low consumptionand/or for reducing consumption. The server and/or manifold itself canalso be configured to store data indicative of money amounts theconsumer anticipates will be used each month and notify the consumer ofamount used for budgeting. Amounts may be carried over to the nextmonth, just as minutes are carried over in prepaid cell phones. Theamount can be loaded by consumer using a dashboard, cell phone, smartphone, or other device capable of interfacing with the manifold sensor.Prepaid amounts can be deposited with the water company or propertyowner and used for payment of consumer usage. For instance, a user canset or budget water flow rates and volumes for specific ports. Forexample, port serving irrigations systems can be set for optimum ratesand volume for plant types or turf types and shall turn off if a flowrate or volume is exceeded that may be cause by a broken sprinkler headthat allows massive flow at the break. Also, a user can limit water ormeter water to a rented room or apartment or home.

The flow-sensing manifolds and dashboards described in this disclosurecan enable water credits trading similar to the cap and trade systemproposed for carbon emissions. Water pricing is based on a tier usagesystem. If consumers are aware of their consumption and know what theyhave yet to consume in a lower tier, they may elect to trade or sell thewater they have yet to use in the lower tier to a person or businessthat is nearing a higher tier rate for that month. Immediate awarenessof usage and remaining amounts give consumers the ability to trade/selltheir remaining lower tier usage rates to higher consumers at the lowertier rates. It also gives consumers the ability to receive an additionreward from the sale of what they conserve to higher users. Higher usershave the ability to purchase unused capacity at lower rates compared tothe higher tier rates charged by the utility company. Knowledge of usageshould promote conservation and result in greater rewards for those whoconserve. Public utilities, property owners, and property managers maybenefit as well due to overall lower consumption. A public exchangecould be established in the city or private exchanges could be developedfor multi-tenant buildings, such as apartment buildings and shoppingmalls, where tenants of the same building trade water credits.

The dashboards can provide advice to consumers about how to lower waterconsumption (or fluid consumption, in general) and alerts relating tothe condition of the plumbing. For instance, the dashboards can provideinstructions to reduce consumption by changing dishwasher or otherappliance settings. The dashboards can also recognize and alert usersabout changes in flow due to leaks, frozen pipes, flooding,malfunctioning appliances, and other maintenance conditions. Manifoldswith flow rate sensors integrated thereon can also be used to detectunauthorized water usage, e.g., in vacant apartments. The dashboard mayalso predict potentially damaging situations, such as freezingtemperatures, by combining water usage data with data derived from othersources or other sensors (such as temperature sensors integrated in themanifold). In some implementations the processor(s) can transmit alertsvia the wireless interface to the fire department, emergency services,property owner, utility company, and/or insurance company based on, forinstance, measured flow rate and other sensor data indicating fire,flooding, or another disruption in service.

The dashboards can also provide advertisements and/or links to websitesthat can provide fluid distribution system maintenance or repairservices. In some implementations, targeted advertisements for waterappliances or plumbing fixtures can be generated based on data collectedabout individual fixture usage over time and overall water consumption.The choice of which advertisements to include for these services andproducts can be made based on an advertising service that maintenanceservice providers subscribe to. The frequency of relevant advertisementoccurrence could be based on level of subscription, i.e., highersubscription rate for a particular subscriber results in a more frequentoccurrence of that subscribers advertisements on the dashboard.

In some implementations, manifolds described herein can be employed forautomatically monitoring fluid flow and/or fluid usage by differentapparatuses (such as fixtures and/or appliances) associated with (orcoupled to) a fluid distribution system (such as a water distributionsystem). Fluid flow detected by the manifolds can be classified based ondifferent fluid flow events (such as water flow events). While examplesdescribed in this disclosure relate to water distribution systems, thedevices (such as manifolds) and flow monitoring methods (such asdetection and estimation of fixture/appliance fluid usage methods)described in this disclosure can be employed with regard to other fluiddistribution systems (e.g., a natural gas distribution system).

FIG. 10A shows a plot depicting an example water flow rate 1010 overtime associated with time overlapping water flow events. The water flowevents shown in FIG. 10A include “shower open,” “toilet open,” “faucetopen,” “faucet close,” “toilet close,” and “shower close.” In general,the “open” events indicate start of water flow driven by a fixture orappliance such as toilet flush, shower, faucet, washing machine,dishwasher, etc. The “close” events indicate end of water flow driven bythe fixture or appliance. From the plot in FIG. 10A one can see that“open” and “close” are associated with (e.g., preceded or followed by)sharp variation in the detected water flow rate. At any given point intime, the water flow rate 1010 (for example detected at an outlet portof a manifold) represents the cumulative water flow rate for thefixtures and/or appliances driving water flow (through the outlet port)at that point of time.

FIG. 10B shows multiple plots of probability distributions (e.g.,probability density functions) for water usage per appliance/fixtureevent. The probability density function 1015 illustrates the probabilitydistribution of the water amount used during a single water flow eventdriven solely by a sink. The probability density function 1025illustrates the probability distribution of the water amount used duringa single water flow event driven solely by a toilet flush. Theprobability density functions 1035, 1045, and 1055 illustrate theprobability distribution of the water amount used during a single waterflow event driven solely by a dishwasher, a washing machine, and ashower, respectively. The probability distributions shown in FIG. 10Bexhibit a Gaussian (for Gaussian-like) behavior.

In some implementations, a processor (such as a processor associatedwith a manifold, flow meter or computer device) can employ measuredwater flow rate (or fluid flow rate, in general) and informationindicative of the probability distributions of water usage per apparatus(such as appliance or fixture) event to determine which apparatusescontributed to the measured fluid flow and/or the fluid amount used byeach apparatus. For instance, the processor can estimate the cumulativefluid amount passing through an outlet port or a lumen during a timeinterval based on multiple measurements of the fluid flow rate at theoutlet port or the lumen. Using the estimated cumulative fluid amountand the probability distributions of water usage per apparatus such asappliance or fixture) event, the processor can desegregate thecontribution of each apparatus to the estimated cumulative fluid amount.

Considering the probability distributions shown in FIG. 10B, the meanvalues for water usage per apparatus event are 0.66 gallon G, 1.6 G,15.0 G, 20.0 G, and 25.0 G for the sink, the toilet flush, the dishwasher, the washing machine, and the shower, respectively. Using suchmeans, the processor can estimate a cumulative water flow usage amountdriven by a combination of these apparatuses asCU=S×0.66+T×1.6+D×15.0+W×20.0+B×25.0, where S, T, D, W, and B representthe number of sink, toilet, dish washer, washing machine, and showerevents, respectively, that could contribute to driving water flow duringa given time interval. In some implementations, the processor can usesuch mathematical representation (of the cumulative water flow usageamount) to detect which fixtures and/or appliances contributed todriving a measured water flow usage amount within a given time interval.

Using the computed amount of used water for a given event and the meansof the probability density functions shown in FIG. 10B, the processorcan determine the combination of fixtures and/or appliances that likelycontributed to driving water during the recorded event. For instance,the processor can evaluate various values of cumulative water usage CUusing different possible combinations of fixtures and/or appliances. Theprocessor can then compare the evaluated CU values to the computedamount of water used (or consumed) during the event. A combination offixtures and/or appliances with a respective CU that is close to thecomputed total amount of water used during the event is likely to be thecombination driving water flow during the recorded event.

FIGS. 11A-11C show plots illustrating water flow rates recorded forvarious water flow events driven by fixtures and/or appliancesassociated with FIG. 10B. That is, the water flow events shown in FIGS.11A-11C are driven by a combination of apparatuses (among those shown inFIG. 10B) with respective probability distributions of water usage perappliance/fixture event as shown in FIG. 10B. For each of the waterevents shown in FIGS. 11A-11C, the processor can compute (or estimate)the total amount of water used in each event based on respective flowrate measurements. For instance, the processor can integrate the plotrepresenting measured flow rate for each event to compute the respectivetotal amount of fluid used. The processor can employ any numericalintegration method such as trapezoidal rule based integration, Riemannsums, or any other numerical integration method known in the field ofmathematics. When computing the total amount of water used in a givenevent, the integration is applied to a complete event (e.g., a recordedflow rate plot, or a sequence of flow rate measurements, that starts andends with a flow rate substantially equal to zero GPM). That is, thearea considered for integration is an area defined by a plot startingand ending at the x-axis.

Considering the water flow event shown in FIG. 11A, the total amount ofwater used during that event can be computed (e.g., by the processor) tobe equal to 1.7 gallons. The processor can evaluate the mathematicalexpression CU=S×0.66+T×1.6+D×15.0+W×20.0+B×25.0 with different integervalues for S, T, D, W and B. For instance, for [S T D W B]=[1 0 0 0 0],CU=0.66, for [S T D W B]=[2 0 0 0 0], CU=1.32 and for [S T D W B]=[0 1 00 0], CU=1.6. The processor can then compare the evaluated CU values to1.7 G (the total amount of water used during the event), for instance,by computing respective percentage mean squared errors (MSE). Thecomputed percentage errors are 5% error for [S T D W B]=[0 1 0 0 0], 22%error for [S T D W B]=[2 0 0 0 0], and 5% error for [S T D W B]=[0 1 0 00]. The processor can select the combination with the smallest error asthe one that drove the water flow during the recorded water flow event.In the above example, only three combinations were considered as othercombinations would obviously result in much larger errors. In someimplementations, the processor can employ measures of errors such asmean squared error (MSE), L1 norm error, or other measures of error tocompare evaluated CU values with the computed total amount of waterused. The combination with the least MSE can be selected asrepresentative of the combination of fixtures and/or appliances drivingthe recorded water flow event. In the example shown in FIG. 11A theweight vector representative of a single toilet flush results in theleast MSE.

For the water flow event shown, in FIG. 11B, the total amount of waterused is 0.72. The percentage errors for [S T D W B]=[0 1 0 0 0], [S T DW B]=[2 0 0 0 0], and [S T D W B]=[0 1 0 0 0] are 8%, 83% and 122%,respectively. In this example, only three combinations were consideredas other combinations would obviously result in much larger errors.Errors for other combinations that are not evaluated are obviouslylarger than those shown in this example. Based on these error values,the processor can select the combination with a single sink as the onethat drove the water flow during the recorded water flow event.

Considering the water flow event shown in FIG. 11C, the total amount ofwater used is 2.42 gallons. The percentage errors for [S T D W B]=[3 0 00 0], [S T D W B]=[0 1 0 0 0], and [S T D W B]=[1 1 0 0 0] are 18%, 83%and 6%, respectively. Based on these error values, the processor canselect the combination with a single sink as the one that drove thewater flow during the recorded water flow event. In this example, errorsfor only three combinations are shown for the sake of brevity. Othercombinations would obviously result in larger errors.

When evaluating the cumulative water usage CU and respective errors fordifferent combinations of fixtures and/or appliances, the processor candetermine the number of combinations to consider based on, for example,the computed total amount of water used based on the flow ratemeasurements, the time duration of the recorded water flow event, thetotal number of a given fixture or appliance coupled to the waterdistribution system (e.g., the total number of sinks in an apartment).For instance, in the water flow event shown FIG. 11B, given that thetotal amount of water used during that event is 0.72 gallons, theprocessor can avoid evaluating combinations involving a dish washer,washing machine or shower as water events driven by thesefixtures/appliances are associated with water usage values that are muchgreater than the total amount of water associated with the recordedevent.

FIG. 12 shows multiple plots of water flow rate probabilitydistributions (e.g., probability density functions) associated withvarious fixtures and appliances. That is, for each appliance or fixture,the respective probability density function represents the probabilitydistribution of the water flow rate (at any given point of time) forthat appliance or fixture. In general, a cumulative water flow rate(such as the water flow rate 1010 shown in FIG. 10A) can be viewed atany given point of time as a weighted sum of individual water flow ratesof fixtures and/or appliances that are driving water flow at that pointof time through a given port. For instance, given that the means of theflow rates for the sink, the dish washer, the toilet flush, the washingmachine and the shower are 0.5 GPM, 1.0 GPM, 1.3, GPM, 2.0 GPM and 2.5GPM, respectively, a cumulative flow rate driven by a combination ofthese fixtures and appliances, at a given time instance, can berepresented as CR=S×0.5+D×1.0+T×1.3+W×2.0+B×2.5, where S, D, T, W, and Brepresent the number of sinks, dish washers, toilet flushes, washingmachines, and showers that are simultaneously driving water at that timeinstance.

In some implementations, given a flow rate measurement, the processorcan employ the mathematical formulation for the cumulative flow rate CRto determine the most likely combination of apparatuses (e.g., fixturesand appliances) that are driving the water flow at the time instance theflow rate was measured. For instance, similar to the examples discussedwith respect to FIGS. 11A-11C, the processor can compare the CR valuesevaluated for various combinations of fixtures and/or appliances withthe measured flow rate value(s) by computing and comparing respectiveerrors. The processor can select the combination of fixtures and/orappliances resulting in the least error as the one likely to have driventhe water flow at the time the flow rate is measured.

In general, the problem of identifying which device(s) are driving fluidflow (also referred to as device identification/fingerprinting problem)during a recorded fluid flow event, or at the time a flow rate value ismeasured, is a detection problem for detecting a vector state (or aplurality of ON or OFF states) for a plurality of devices based onobservation data. The observation data can include total flowmeasurements (as discussed with respect to FIGS. 10B and 11A-11C) orflow rate measurements (as described with respect to FIG. 13). In someimplementations, other fluid flow event features such as event timeduration, manifold outlet port associated with each measured flow datavalue and/or other features can be used in addition to measured flowdata to determine a combination of devices that were active at the timeflow data was measured. A home or building monitoring system couldconsist of one or more water flow sensors (meters) and any number ortype of other external sensors that all communicate to a centralized ordistributed fingerprinting/identification application. The measured datacould include, but is not limited to (1) water flow usage data, (2)water flow rate data, (3) data from other sensors in a home or building,(4) and/or information indicative of flow sensors associated with eachrespective measured flow data set measurement.

In general, flow sensors can measure different flow parameters, such asduration of flow events, peak water flow rate during a particular timeinterval, average flow rate during a particular time interval,instantaneous flow rate during a time interval, slope of accumulationduring a particular time interval, instantaneous flow rate slope duringa particular time interval, interval between active flow events, shapeof flow rate versus time, both onset and release events, the like, orcombinations thereof.

The flow monitoring system may also collect data from other sensors suchas, door sensor(s) throughout a home indicating open/close, infraredsensor(s), light sensor(s), acoustic sensors placed throughout ahome/building, accelerometers placed in pipes/handles of appliances,humidity sensors, ambient air temperature sensors (e.g., bath indoor andoutdoor placement), water activated switches (water between twoelectrodes), toilet seat sensors, and/or other sensors. The monitoringsystem can also record other information such as season/month of theyear, day of the week, time of the day, geographic location,gender/age/number of occupants in home or building, health conditions ofhome occupants, current weather conditions from, occupation state of thehome or building, and/or other side information.

In some implementations, using training data with information indicativeof active plumbing fixtures and water-using appliances when a particularset of measured data is observed, a processor (e.g., associated with acomputer device) can generate (or estimate) a statistical model for usein detecting (or identifying) active devices based on measured data. Thecomputer device (such as a server) can employ statistical techniquessuch as histograms, kernel density estimation, Gaussian mixture models(GMMs), hidden Markov models (HMMs), factorial HMMs, or otherstatistical modeling methods. Specifically, let Y be a vector of anycombination of measured data (such as fluid flow measurements, othersensor(s) data and/or other data as described above) and let X be adevice-events vector indicative of active devices, or a number thereof,in a monitored fluid distribution system (such as a water distributionsystem of a building or an apartment). Generating the statistical modelcan include estimating the probabilities p(Y|X) and/or p(X|Y) forvarious instances of X and Y. For instance, generating the statisticalmodel can include estimating the probability density function (pdf) (orprobability mass function (pmf)) of the conditional random variable Y|X.In some implementations, the statistical model can be a classifier thatallows mapping each observation vector Y to a respective device-eventsvector X. In some implementations, such classified can be configured toprovide a respective probability value p(X|Y) when mapping an instanceof the observation random vector Y to the device-events vector X.

The vector X can be a vector of (1) binary random variables (each takesa value of 0 or 1) representing the states (such as active or inactive)of each or a subset of the fluid-using devices coupled to fluiddistribution system (such as a water distribution system in a home orbuilding), (2) integer random variable(s) representing the number ofevents per device or the number of active devices, or (3) a combinationof both device states and number of active devices. The observationrandom vector Y can include measured flow rate values (as discussed withregard to FIG. 13) from one or more flow sensors (e.g., at a timeinstance or over a time interval), measured flow velocity values (e.g.,at a time instance or over a time interval), measured total fluid usageper event (as discussed with respect to FIGS. 10B and 11A-11C), flowevent time duration, or a combination thereof. The observation randomvector Y can also include measured fluid pressure values, measuredtemperature values, other sensor data, other information (such asinformation indicative of device(s) served by each manifold outlet portand corresponding flow sensor) or a combination thereof.

If the p(Y|X) are known (e.g., estimated as part of the statisticalmodel) for each possible instance of X, then a processor can employmaximum likelihood (ML) estimation to determine which water events Xoccurred given that Y is observed (or measured). The ML estimationprocess (or application can be executed by a processor associated with amanifold, flow meter, a smartphone, tablet computer, or server. The MLestimation application can obtain the observation instance of Y from aflow meter (such as ultrasonic sensor) and map the observation instanceof Y to an instance of X that maximizes the probability p(Y|X). That isX_(ML)=argmax_(X)ρ(Y|X). In the case where the probability p(X) for eachX is known pdf or pmf of X is known or estimated as part of thestatistical model), then the processor can employ a maximum a posterioriprobability (MAP) estimation. When employing MAP estimation, theprocessor is configured to select the instance of X that maximizesp(Y|X) p(X). That is X_(MAP)=argmax_(X)ρ(Y|X) ρ(X). When generating (orestimating) a Gaussian mixture model (GMM), the processor can employtraining data to model the conditional random vectors X|Y using Gaussianmixtures (for Gaussian clusters) and estimate respective Gaussianprobability distributions. Each Gaussian mixture or cluster can be aassociated with a respective instance of the vector X. Given anobservation instance y of the random vector Y, the processor can selectthe instance of X associated with the cluster leading to the highestp(y|X).

In some implementations, the processor can model states of the devices,that can drive fluid flow through one or more outlet ports or lumensusing one or more hidden Markov chains. For instance, the processor canrepresent any sequence of flow events over time using a single hiddenMarkov chain such that each state in the chain corresponds to arespective instance of the vector X at a respective time instancet±k×Δt, where k is an integer value, t is a reference time instance andΔt is a time period (e.g., representing flow data measurement period).Each state of hidden Markov chain has an output corresponding topossible observation data. In modeling the HMM, the processor can beconfigured to estimate the probability distributions for the HMM stateoutputs. Such probability distributions represent the conditionalprobabilities p(y|X). The processor can also estimate transitionprobabilities between different states or use predefined values for suchtransition probabilities.

FIG. 13 shows a flow diagram illustrating a factorial hidden Markovmodel (FHMM). In some implementations, the processor can model states ofthe devices, that can drive fluid flow through one or more outlet portsor lumens using a FHMM. In a FHMM, the states of each device over timeare represented using a respective separate hidden Markov chain. Asshown in FIG. 13, given that the total number of devices that drivefluid flow in a monitored system is N, the corresponding FHMM includes Nhidden Markov chains running in parallel. Each hidden Markov chaincorresponds to a respective device. At any given time instance t±k×Δt,the observation data (e.g., an instance of the vector or elementsthereof) corresponds to one or more cumulative sums of outputs fromdistinct states in the N hidden Markov chains. For instance, whenmultiple flow sensors are employed (such as in association with multipleoutlet ports of a manifold), multiple cumulative sums are generated bythe FHMM at a given time instance. For each flow sensor at a givenoutlet port, a respective cumulative sum can be generated (by the FHMM)as the sum of outputs from hidden Markov chains associated with devicesserved by that outlet port.

FIG. 14 shows a flow chart illustrating a process 1400 for estimating astatistical model or device signatures. The process 1400 includesobtaining training data segments 1401 and respective metadata 1405. Thetraining data segments 1401 can include measured flow rate data segments(or measured flow velocity data segments) obtained from one or more flowsensors integrated in a fluid distribution system (such as a waterdistribution system). In some implementations, the flow sensor(s) can beintegrated at one or more lumens (such as pipe(s) or tailpiece(s)) in afluid distribution system. In some implementations, the flow sensors canbe integrated in one or more manifolds (such as described with respectto FIGS. 1A, 1B, 3A, 3B, 4, 5A-5D, 6, 7A, 7B, 8A and 8B). The trainingdata segments can include a plurality of measurement samples for eachdevice type (such as a sink, shower, toilet flush, dish washer, washingmachine, etc.) operated solely in active mode (i.e., driving fluidflow). Using a plurality of distinct measurement samples (e.g.,associated with various flow events driven solely by the same device)for each device allows for reliable estimation of the distribution ofthe conditional probabilities p(Y|X).

The metadata 1405 can include user-provided data indicative of activedevice(s) at various time instances, data indicative of the flow sensorassociated with each data segment 1401 (in case multiple flow sensorsare employed) and respective devices served by the lumen where the flowsensor is located, other sensor data (such as temperature measurements,fluid pressure measurements, etc.), time information (such as time ofday), etc. The user can be a directed-study pilot participant, consumer,or industrial end user. The metadata 1405 can be entered through aseparate or integrated interface application running on a smartphone ortablet computer. For example, the user can first enter information aboutthe number of plumbing fixtures and water-using appliances in a home orbuilding. The user can also enter a subset of the side information, suchas number of occupants and occupation state of the home or building. Theuser can then use the interface application to tag water events inreal-time as they occur, including the type and estimated duration. Theinterface application can communicate such metadata 1405 to a trainingapplication running on a server or computer device. The data segments1401 paired with the metadata 1405 represent the training data. In acase where multiple flow sensors are integrated at each output lumen ofone or more manifolds and each output lumen is arranged to serve onerespective fluid using device, information indicative of the outputlumen (or the flow sensor therein) associated with each data segment1501 can allow separating the data segments 1501 based on respectivefluid using devices. The data segments 1501 and the metadata 1505 can bearranged in separate data files or combined in one or more data files.

As different flow events occur, the training application can generate aset of p(Y|X) and p(X). In some implementations, probability densityfunctions associated with flow events involving a combination of devicesbeing active simultaneously can be computed by convolving probabilitydensity functions associated with respective events involving a singledevice each. For instance, the pdf for Y|X=[1 1 0 0 0] can be obtainedas the convolution for the pdf of Y|X=[1 0 0 0 0] and the pdf for Y|X=[01 0 0 0]. In some implementations, the training data can include datasegments 1501 for all possible combinations of devices. In suchimplementations, the pdfs associated with events involving a combinationof devices driving fluid flow simultaneously can derived directly fromthe training data.

The process 1400 can include training a statistical model, for instance,by estimating respective parameters (step 1410). The statistical modelcan include a maximum likelihood (ML) estimator, maximum a-posterioriestimator, Gaussian mixture model (GMM), hidden Markov model (HMM),factorial HMM (FHMM), or other statistical model. Using the datasegments 1401 and the metadata 1405, the training application canestimate internal parameters of the statistical model (such asprobability density functions for observations constrained to a givendevice-events vector (i.e., Y|X), probability mass functions for X,observation clustering intervals (or regions), transition probabilitiese.g., for HMM and FHMM), or a combination thereof. The trainingapplication can estimate such internal parameters through iterative orone-step procedures.

The process 1400 can also include extracting other features from thetraining data and classifying the extracted features (step 1420). Forinstance, the training application can extract features such as timeduration, total fluid usage, mean flow rate, number of peaks, maximumflow rate, minimum flow rate or other features associated with each flowevent. The training application can then classify the extractedfeatures, for instance, per single-device events. For example, the timeduration of toilet flush flow events can be typically shorter thanshower flow event or washing machine flow events. Accordingly,information indicative of event time durations can help segregate (orclassify) various device flow events. The training application canemploy any classification technique known in the art. In someimplementations, the feature extraction and classification step can beoptional.

The training application is configured to output a trained statisticalmodel (e.g., ML estimator, MAP estimator, GMM, FHMM, etc.). The trainingapplication can also provide per device signatures such as featureclassification results, observation data intervals (or regionsassociated with each device, or a combination thereof). The trainedstatistical model and/or the device signature information can beemployed by a computer device or processor to estimate flow events basedon recorded observation data.

FIG. 15 shows a flow diagram illustrating a process 1500 for estimatingflow events based on observation data. The process 1500 can includeobtaining a data block by a processor. The data block can be hours long(such as 8 hours long, 10 hours long, 12 hours long, 24 hours long or ofother duration). The data block can include one or more sequences e.g.,associated with distinct flow sensors) of flow sensor measurement data(such as flow rate measurements or flow velocity measurements),measurement data from other sensors (such fluid pressure measurements,temperature measurement data, etc.) and/or other data such dataindicative of flow sensors or lumens/ports associated thereof).

The process 1500 can include the processor segmenting a sequence of flowsensor measurements into one or more segments (step 1520). The processorcan segment a sequence of flow sensor measurement data based on, forinstance, a flow rate (or flow velocity) threshold value (e.g., 0.05GPM, 0.1 GPM, 0.2 GPM, or other flow rate value defined based onmeasurement error information of flow sensor(s)). That is, the processorcan segment a flow sensor measurement data sequence at intervals whereflow rate (flow velocity) values are below the threshold value. In someimplementations, the processor can re-combine neighboring segments ofthe interval during which flow rate (or flow velocity) values are belowthe threshold is shorter than o time threshold value (for instance, ifsuch time interval is shorter than 30 seconds, seconds or other timethreshold value). In some implementations, the processor can re-joinneighboring segments if, for instance, flow rate (or flow velocity)values in both segments are similar (e.g., having means and varianceswithin a difference margin). In some implementations, the datasegmentation step 1520 allows for generating data segments 1521 thatrepresent complete flow events (e.g., with flow rate values starting andending below the flow rate threshold). As such, a segment 1521 is likelyto represent measurement data for a single-device flow event or amulti-device flow event with time overlap between fluid flows driven bymore than one device. In some implementations, the processor canclassify data segments 1521 associated with single-device flow eventsbased on respective features. For instance, the processor can extractfeatures such time duration, total fluid usage, mean flow rate, numberof peaks, maximum flow rate, minimum flow rate or other featuresassociated with each data segment. The processor can then determinewhether the extracted features are indicative of a given single-deviceevent, based for instance, on feature classification results obtained atstep 1420 of FIG. 14. If the extracted features are indicate that therespective segment represents a specific single-device flow event, theprocessor can store the estimated single device event in a memory ordatabase.

The process 1500 can include the processor feeding observation data in asegment 1521 to a trained statistical model (such as HMM, FHMM or GMM,ML estimator or MAP estimator) to estimate one or more respective flowevents. In some implementations, the trained statistical model can beconfigured to generate a flow event estimate for each piece observationdata associated with a respective time instant. In some implementations,the trained statistical model can be configured to generate a flow eventestimate for each piece of observation data associated with a respectivetime interval. In some implementations, step 1540 is performed only ifthe processor fails to estimate the flow event based on the segmentisolation step 1530. In some implementations, the segment isolation step1530 can be optional. In such implementations, only the statisticalmodel or device signature information can be employed to estimaterecorded fluid flow events.

In some implementations, the processor can be configured to use eventestimation information to estimate cumulative water usage per device.For instance, the processor can be configured to estimate total waterusage by the toilet flush, the sink, the washing machine, etc.

In some implementations, the process 1500 for estimating flow events canbe executed by a cloud server, a computer device or a client device(such as a tablet, mobile device or laptop, desktop). In someimplementations, a simple version of the process 1500 (such as a versionemploying ML or MAP estimation to measured flow rate values) can berunning on a processor associated with a flow meter or a manifold.

A person skilled in the art would readily appreciate that the flow eventestimation methods described in this disclosure can be implemented incombination with (or independent of) manifolds described above. Also, aperson skilled in the art would appreciate that the flow eventestimation methods should not be limited to water distribution systems,but rather can be employed for other fluid distribution systems (such asnatural gas distribution systems).

In some implementations, the processor can employ historical data toestimate the pdf for the conditional random variable Y|X. For example,distributions that describe the average flow rate of various plumbingfixtures and/or water-using appliances can be derived from water usagestudies that have been conducted by the Water Research Foundation. Underthe assumption that flow events associated with distinct devices arestatistically independent, the probability p(Y|x₁, . . . x_(i)) can becomputed using the convolution the pdfs of p(x₁), . . . p(x₁). Thisprocess can be repeated for each different flow events involvingcombinations of devices simultaneously active, resulting in a set ofpdfs for Y|X that can be used in ML estimation. This technique can alsobe extended to multivariate distributions, e.g., historical data thatincludes average flow, flow duration, and peak flow.

Once an initial statistical model is created using training and/orhistorical data, a processor can employ such statistical model for flowevent identifications. The initial identifications can be communicatedback to a user training application. The user can then provideinformation about the correctness of these identifications, e.g.,confirming/disconfirming that a toilet was flushed, the dishwasher isrunning, or the faucet was turned on. The confirmation information canthen be communicated back to the training application. The trainingapplication can update the statistical model based on such confirmationdata. For instance, the training application can update the relevantpdfs of Y|X accordingly. Bayesian learning techniques could be utilizedfor this update process. Online learning, such as Kalman filtering,could also be applied.

The training and event estimation/detection applications operating for aparticular user can communicate with a higher-level training applicationthat may be on a central server or distributed in a cloud computingenvironment. This higher-level training application could be used togenerate generalized (relevant to multiple users) p(Y|X) and P(X)distributions for various fluid events. These generalized statisticalmodels could then be used to refine the individual user statisticalmodel. Additionally, aggregation of the measured data from multipleusers that specifically includes side information about type of plumbingfixtures and water-using appliances present in a home or building canenable device-level fingerprinting. For example, statistical models thatdescribe the average flow rate, flow duration, flow onset and releaseevents, and peak flow characteristics of a plumbing fixture orwater-using appliance can be created.

Other approaches for identifying entities driving water flow at a givenpoint of time may employ device flow-rate templates based on training onknown devices. Templates are time-domain waveform snippets of flowrates. Each template corresponds to a known device. Templates can begenerated through supervised training by customer or unsupervisedtraining by statistical clustering. Templates can then be used as basisvectors instead of using average flow rate values. Using, for example,least MSE techniques, measured aggregate signals (corresponding tosimultaneous water flow events) can be decomposed into individual basisvectors corresponding to each device. For instance, waveletdecomposition can be used to represent single device flow rate signals(or templates) as weighted SUMS of wavelets. Other approaches includetemplate matching or cross-correlation of known templates with measuredflow rate. Template-based approaches yield time-varying measure ofdegree to which device signature is present in the measured flow ratesignal.

In some implementations, the flow monitoring system includes additionalsensors on drain line(s) to confirm which appliance and/or fixtures wasused. Accelerometers, microphones, or other devices can be used tomonitor drain lines. Knowing the difference in time between when waterflow is detected at sensor, and when the drain line detects flow can beused to uniquely identify devices within a home. For example, a showeron the 2nd floor would take longer to show up at the drain than a showeron the 1st floor.

The use of drain line(s) flow information can also be used to identifyfill/empty devices more accurately. A dishwasher and/or washing machine,for example, will show flow, but no drain activity during fill, thenwill show drain activity but no flow during pump-out. The use of drainline(s) flow information can also discriminate between indoor andoutdoor water usage. High flow rates with no drain activity are either apipe burst, or outdoor use. Flow rate can be used to discriminate.

In some implementations of the flow monitoring system, the manifold caneliminate need for handles on faucets, toilets or outlets by usinginformation from other sensors to control manifold ports. Using a motiondetector, a request for flow can be sensed at faucet location. Based ondata from motion detector the manifold can turn on a manifold port toallow flow. In the case that the manifold has shutoff valves on eachoutlet leading to a device, the need for traditional hot and cold waterhandles for appliances such as faucets, showers, and toilets may beovercome. Instead of handles, wired or wireless sensors such as motiondetectors, touch sensors, etc. can trigger the smart manifold to turn onand off individual appliances. For example, a shower may turn onautomatically when you enter it and turn off automatically when youleave it. A toilet could flush by waving your hand over a sensor locatedon the toilet and the sink could turn on simultaneously.

According to some implementations, the manifold may be trained to presetthe temperatures and flow rates of preference for each appliance throughan application on a smart phone, tablet, or computer. A user can, forexample, slide a bar or other input widget on a temperature gauge in theapplication while simultaneously feeling the temperature of theappliance under training until the desired temperature and flow rate ismet. The manifold can then store desired temperature and flowinformation in memory to be recalled when activating device. Theapplication can also allow for rules to be set when linking sensorevents to appliance water control.

Appliances with handles may be retrofitted to allow such functionality.The user may use the hot and cold faucet handles to set the desiredtemperature and flow rate and notify the manifold through theapplication. The manifold can save the current flow rate for thecorresponding hot and cold outlets. When the user is finished with thetraining, he/she would leave the appliance handles in the full openposition and the manifold would take responsibility of controlling theappliance water using the saved flow rates during training.

In other implementations, humidity sensor in bathroom can be used todetect a particular shower is used (corroborated with flow data). Othersmart devices, e.g., motion detectors, that localize occupant activityto a specific device (e.g. motion in kitchen plus correlated flow rateindicates kitchen sink), contact switch that indicates interior orexterior doors are activated followed by flow rates correlated to aspecific device type can be used to resolve whether a device is in coulddoor or outdoor, or which room in a home is most likely correlated toobserved flow.

A training system can be developed that uses combinations of the abovemulti-sensor techniques to map specific sensor combinations to specificdevices. A user with a handheld device, laptop, etc. can individuallyactivate and identify given devices. As an example, in the specific caseof drain and flow sensor, the user could flush upstairs toilet in MasterBedroom, used GUI on smart device to start and stop recording event,fingerprint for that device could be memorized for that specificuser/home/device. Slight differences in flow rates, accumulation, andrelative timing between flow and drain activity could distinguishbetween devices due to differences in each of these measurements. Thisinformation can also be correlated with other sensors (motion, video,contact switches, humidity sensors, etc.) individually or in groups toenhance selectivity/accuracy of device identification.

It should be understood that the systems described above may providemultiple ones of any or each of those components and these componentsmay be provided on either a standalone machine or, in some embodiments,on multiple machines in a distributed system. In addition, the systems,methods, and engines described above may be provided as one or morecomputer-readable programs or executable instructions embodied on or inone or more articles of manufacture. The article of manufacture may be afloppy disk, a hard disk, a CD-ROM, a flash memory card, a PROM, a RAM,a ROM, or a magnetic tape. In general, the computer-readable programsmay be implemented in any programming language, such as LISP, PERL, C,C++, C#, PROLOG, or in any byte code language such as JAVA. The softwareprograms or executable instructions may be stored on or in one or morearticles of manufacture as object code.

While the invention has been particularly shown and described withreference to specific embodiments, it should be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention asdefined by the following claims. For example, the manifolds disclosedherein may be used to monitor flow rates of fluids other than water,such as oil, gasoline, etc.

What is claimed is:
 1. A fluid flow monitoring system for identifyingfluid flow events comprising: an ultrasonic sensor for generatingmeasurement signals associated with respective ultrasonic signalspropagating through a fluid in a chamber of a fluid distribution system,the fluid distribution system coupled to a plurality of devices capableof causing fluid flow through the chamber; a memory storing datarepresentative of a plurality of device fluid flow signatures, eachdevice fluid flow signature including statistical parameters defining acorresponding probability distribution of fluid flow rate for acorresponding device of the plurality of devices coupled to the fluiddistribution system; and one or more processors communicatively coupledto the ultrasonic sensor and to the memory, the one or more processors:computing a sequence of fluid flow rate values associated with a fluidflow event based on a plurality of measurement signals generated by theultrasonic sensor; determining, for each device of the plurality ofdevices, a corresponding first conditional probability valuerepresenting the probability of the sequence of fluid flow rate valuesgiven that the fluid flow event is caused by the device, using thestatistical parameters defining the probability distribution of fluidflow rate for the device; and identifying, using the first conditionalprobability values, a first device of the plurality of devices causingthe fluid flow event.
 2. The fluid flow monitoring system of claim 1,wherein the plurality of device fluid flow signatures further include,for each combination of two or more devices, corresponding statisticalparameters defining a corresponding probability distribution of fluidflow rate for that combination of devices.
 3. The fluid flow monitoringsystem of claim 2, wherein the one or more processors: determine, foreach combination of devices, a corresponding second conditionalprobability value representing the probability of the computed sequenceof fluid flow rate values given that the fluid flow event is caused bythe combination of devices, using the statistical parameters definingthe probability distribution of fluid flow rate for the combination ofdevices; and identify, using the first conditional probability valuesand the second conditional probability values, the first device or afirst combination of devices causing the fluid flow event.
 4. The fluidflow monitoring system of claim 3, wherein identifying the first deviceor the first combination of devices, using the first conditionalprobability values and the second conditional probability values,includes using maximum likelihood detection.
 5. The fluid flowmonitoring system of claim 3, wherein identifying the first device orthe first combination of devices includes using a maximum a posterioriprobability model.
 6. The fluid flow monitoring system of claim 1,wherein, for each device of the plurality of devices, the device fluidflow signature further includes an indication of a corresponding deviceevent duration.
 7. The fluid flow monitoring system of claim 1, wherein:identifying the first device includes employing a detection modelgenerated based on the first probability values.
 8. The fluid flowmonitoring system of claim 7, wherein the detection model includes aGaussian mixture model.
 9. A method identifying fluid flow events in afluid distribution system, the method comprising: generating, by anultrasonic sensor of a fluid flow meter, measurement signals associatedwith respective ultrasonic signals propagating through a fluid in achamber of the fluid distribution system, the fluid distribution systemcoupled to a plurality of devices capable of causing fluid flow throughthe chamber; storing, by a memory of the fluid flow meter, datarepresentative of a plurality of device fluid flow signatures, eachdevice fluid flow signature including statistical parameters defining acorresponding probability distribution of fluid flow rate for acorresponding device of the plurality of devices coupled to the fluiddistribution system; computing, by one or more processors of the fluidflow meter, a sequence of fluid flow rate values associated with a fluidflow event based on a plurality of measurement signals generated by theultrasonic sensor, the one or more processors communicatively coupled tothe ultrasonic sensor and to the memory; determining, by the one or moreprocessors of the fluid flow meter, for each device of the plurality ofdevices, a corresponding first conditional probability valuerepresenting the probability of the sequence of fluid flow rate valuesgiven that the fluid flow event is caused by the device, using thestatistical parameters defining the probability distribution of fluidflow rate for the device; and identifying, by the one or more processorsof the fluid flow meter, using the first conditional probability values,a device of the plurality of devices causing the fluid flow event. 10.The method of claim 9, wherein the plurality of device fluid flowsignatures further include, for each combination of two or more devices,corresponding statistical parameters defining a correspondingprobability distribution of fluid flow rate for that combination ofdevices.
 11. The method of claim 10, comprising: determining, for eachcombination of devices, a corresponding second conditional probabilityvalue representing the probability of the computed sequence of fluidflow rate values given that the fluid flow event is caused by thecombination of devices, using the statistical parameters defining theprobability distribution of fluid flow rate for the combination ofdevices; and identifying, using the first conditional probability valuesand the second conditional probability values, a device or a combinationof devices causing the fluid flow event.
 12. The method of claim 11,wherein identifying a device or a combination of devices, using thefirst conditional probability values and the second conditionalprobability values, includes using maximum likelihood detection.
 13. Themethod of claim 9, wherein, for each device of the plurality of devices,the device fluid flow signature further includes an indication of acorresponding device event duration.
 14. The method of claim 9, wherein:identifying the first device includes employing a detection modelgenerated based on the first probability values.
 15. The method of claim14, wherein the detection model includes a Gaussian mixture model. 16.The method of claim 9, wherein identifying the first device includesusing a maximum a posteriori probability model.